Post-Doctoral Associate—Computational Proteomics Proteomics Platform, Broad Institute of MIT and Harvard

Post-Doctoral Associate—Computational Proteomics Proteomics Platform, Broad Institute of MIT and Harvard hasmik  2021-04-05

The Proteomics Platform at the Broad Institute (Steve Carr, PI) is looking for a motivated post-doctoral associate in computational proteomics who will focus on developing and applying innovative computational approaches to analyzing proteogenomics data spanning genomics, transcriptomics, proteomics, phosphoproteomics and other posttranslational modifications from cancer, cardiovascular, and other diseases being actively studied at Broad. The work will involve analysis of large-scale multi-omics data via pathway and network analysis, integrated with statistical and machine learning approaches, to derive biological insights and identify therapeutic opportunities.
The successful candidate will be part of an interdisciplinary team of proteomics and computational scientists, biologists, and clinicians. He or she will apply computational, statistical and machine learning methods to advance the state of the art in proteomics; develop data analysis strategies, write algorithms, and deploy computational tools for the exploration of large proteogenomics data sets; conceive, implement and test statistical models; work with wet-lab researchers to translate these models into testable experiments; analyze the data produced from these experiments; test and develop novel tools for pathway and network analysis with emphasis on integrating diverse omics data types; and implement algorithms as software for distribution to the global research community.
• The candidate should have a Ph.D in Computer Science, Bioinformatics, Computational Biology, Statistics or a related quantitative discipline. We are specifically looking for a talented and motivated researcher with a proven track record in applying computational methods to the analysis of large-scale omics datasets.
• A strong background in statistics and machine learning with both breadth of knowledge (hypothesis testing, linear models, supervised and unsupervised learning methods) and depth in a specific area (e.g., pathway and network analysis, Bayesian analysis, probabilistic methods, deep learning).
• Proven programming skills with the ability to learn new languages and environments quickly. The group primarily uses R and Python along with shell scripts and other languages as needed, in a cluster and cloud computing environment.
• Exposure to mass spectrometry-based proteomics and/or computational proteomics is a strong plus.
• Excellent ability to learn quickly and strong problem-solving skills, along with effective communication, are essential to successful performance in the fast-changing research computing environment at the Broad Institute.
The Broad Institute provides a vibrant research environment with close links to MIT, Harvard and the Harvard-affiliated hospitals across Boston. Working in the Broad’s Proteomics Platform provides the potential for your contributions to be utilized and recognized across a global network of researchers in mass spectrometry-based proteomics and proteogenomics. The Proteomics Platform is currently a significant contributor to many large consortium projects including the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Molecular Transducers of Physical Activity Consortium (MoTrPAC) among others, with flagship publications in high profile journals.
Interested candidates should send a letter of inquiry and CV to Steven Carr ( and to D. R. Mani (